{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [DecisionTreeRegressor 决策树回归](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "C:\\Users\\Administrator\\Anaconda3\\lib\\site-packages\\sklearn\\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression, LassoCV, RidgeCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.linear_model.coordinate_descent import ConvergenceWarning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def notEmpty(s):\n",
    "    return s != ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['font.sans-serif']=[u'simHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False\n",
    "## 拦截异常\n",
    "warnings.filterwarnings(action = 'ignore', category=ConvergenceWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "样本数据量:506, 特征个数：13\n",
      "target样本数据量:506\n"
     ]
    }
   ],
   "source": [
    "names = ['CRIM','ZN', 'INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT']\n",
    "path = \"datas/boston_housing.data\"\n",
    "## 由于数据文件格式不统一，所以读取的时候，先按照一行一个字段属性读取数据，然后再按照每行数据进行处理\n",
    "fd = pd.read_csv(path, header=None)\n",
    "data = np.empty((len(fd), 14))\n",
    "for i, d in enumerate(fd.values):\n",
    "    d = map(float, filter(notEmpty, d[0].split(' ')))\n",
    "    data[i] = list(d)\n",
    "\n",
    "x, y = np.split(data, (13,), axis=1)\n",
    "y = y.ravel()\n",
    "\n",
    "print (\"样本数据量:%d, 特征个数：%d\" % x.shape)\n",
    "print (\"target样本数据量:%d\" % y.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练数据集样本数目：404, 测试数据集样本数目：102\n"
     ]
    }
   ],
   "source": [
    "#数据的分割，\n",
    "x_train1, x_test1, y_train1, y_test1 = train_test_split(x, y, train_size=0.8, random_state=14)\n",
    "x_train, x_test, y_train, y_test = x_train1, x_test1, y_train1, y_test1\n",
    "print (\"训练数据集样本数目：%d, 测试数据集样本数目：%d\" % (x_train.shape[0], x_test.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始数据各个特征属性的调整最小值: [ -7.10352762e-05   0.00000000e+00  -1.68621701e-02   0.00000000e+00\n",
      "  -7.92181070e-01  -6.82314620e-01  -2.98661174e-02  -1.02719857e-01\n",
      "  -4.34782609e-02  -3.56870229e-01  -1.34042553e+00  -6.38977636e-03\n",
      "  -4.90780142e-02]\n",
      "原始数据各个特征属性的缩放数据值: [  1.12397589e-02   1.00000000e-02   3.66568915e-02   1.00000000e+00\n",
      "   2.05761317e+00   1.91607588e-01   1.02986612e-02   9.09347180e-02\n",
      "   4.34782609e-02   1.90839695e-03   1.06382979e-01   2.53562554e-03\n",
      "   2.83687943e-02]\n"
     ]
    }
   ],
   "source": [
    "#标准化\n",
    "ss = MinMaxScaler()\n",
    "\n",
    "x_train = ss.fit_transform(x_train, y_train)\n",
    "x_test = ss.transform(x_test)\n",
    "\n",
    "print (\"原始数据各个特征属性的调整最小值:\",ss.min_)\n",
    "print (\"原始数据各个特征属性的缩放数据值:\",ss.scale_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#构建模型（回归）\n",
    "# 回归树一般用mse 均方误差，效果是最好的\n",
    "model = DecisionTreeRegressor(criterion='mse', max_depth=7)\n",
    "#模型训练\n",
    "model.fit(x_train, y_train)\n",
    "#模型预测\n",
    "y_test_hat = model.predict(x_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score： 0.828803329919\n"
     ]
    }
   ],
   "source": [
    "#评估模型\n",
    "score = model.score(x_test, y_test)\n",
    "print (\"Score：\", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 0.617726599229\n",
      "lasso: 0.617887746021\n",
      "ridge: 0.620924773165\n"
     ]
    }
   ],
   "source": [
    "#构建线性回归\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "lr_y_test_hat = lr.predict(x_test)\n",
    "lr_score = lr.score(x_test, y_test)\n",
    "print (\"lr:\", lr_score)\n",
    "#构建lasso\n",
    "lasso = LassoCV(alphas=np.logspace(-3,1,20))\n",
    "lasso.fit(x_train, y_train)\n",
    "lasso_y_test_hat = lasso.predict(x_test)\n",
    "lasso_score = lasso.score(x_test, y_test)\n",
    "print (\"lasso:\", lasso_score)\n",
    "#构建岭回归\n",
    "ridge = RidgeCV(alphas=np.logspace(-3,1,20))\n",
    "ridge.fit(x_train, y_train)\n",
    "ridge_y_test_hat = ridge.predict(x_test)\n",
    "ridge_score = ridge.score(x_test, y_test)\n",
    "print (\"ridge:\", ridge_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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SR6JNZG6zMQM6nQ42G9s8n7pqEARBDE6kfZwtBgucFiezjooDCa2heh/n4uJi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04Fg4lCe89roM4V/j9xDofTidugEuf0GIgzRO1YLBLnoAcxLS6Nz27/jIvyJH9q2zGOn/pzr72K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\nhKgFCZyFEEIIIYSohf8P2QcNVRaXe4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a59ba64908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 7. 画图\n",
    "plt.figure(figsize=(12,6), facecolor='w')\n",
    "ln_x_test = range(len(x_test))\n",
    "\n",
    "plt.plot(ln_x_test, y_test, 'r-', lw=2, label=u'实际值')\n",
    "plt.plot(ln_x_test, lr_y_test_hat, 'b-', lw=2, label=u'Linear回归，$R^2$=%.3f' % lr_score)\n",
    "plt.plot(ln_x_test, lasso_y_test_hat, 'y-', lw=2, label=u'Lasso回归，$R^2$=%.3f' % lasso_score)\n",
    "plt.plot(ln_x_test, ridge_y_test_hat, 'c-', lw=2, label=u'Ridge回归，$R^2$=%.3f' % ridge_score)\n",
    "plt.plot(ln_x_test, y_test_hat, 'g-', lw=4, label=u'回归决策树预测值，$R^2$=%.3f' % score)\n",
    "plt.xlabel(u'数据编码')\n",
    "plt.ylabel(u'租赁价格')\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.grid(True)\n",
    "plt.title(u'波士顿房屋租赁数据预测')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 score值: 0.3818844867624794 最优参数列表: {'decision__max_depth': 8, 'pca__n_components': 0.75}\n",
      "1 score值: 0.7090506006084365 最优参数列表: {'decision__max_depth': 11}\n",
      "2 score值: 0.7268140417933565 最优参数列表: {'decision__max_depth': 13}\n"
     ]
    }
   ],
   "source": [
    "#参数优化\n",
    "pipes = [\n",
    "    Pipeline([\n",
    "            ('mms', MinMaxScaler()), ## 归一化操作\n",
    "            ('pca', PCA()), ## 降纬\n",
    "            ('decision', DecisionTreeRegressor(criterion='mse'))\n",
    "        ]),\n",
    "    Pipeline([\n",
    "            ('mms', MinMaxScaler()),\n",
    "            ('decision', DecisionTreeRegressor(criterion='mse'))\n",
    "        ]),\n",
    "    Pipeline([\n",
    "            ('decision', DecisionTreeRegressor(criterion='mse'))\n",
    "        ])\n",
    "]\n",
    "\n",
    "# 参数\n",
    "parameters = [\n",
    "    {\n",
    "    \"pca__n_components\": [0.25,0.5,0.75,1],\n",
    "    \"decision__max_depth\":  np.linspace(1,20,20).astype(np.int8)\n",
    "    },\n",
    "    {\n",
    "    \"decision__max_depth\":  np.linspace(1,20,20).astype(np.int8)\n",
    "    },\n",
    "    {\n",
    "    \"decision__max_depth\":  np.linspace(1,20,20).astype(np.int8)\n",
    "    }\n",
    "]\n",
    "#获取数据\n",
    "x_train2, x_test2, y_train2, y_test2 = x_train1, x_test1, y_train1, y_test1\n",
    "\n",
    "for t in range(3):\n",
    "    pipe = pipes[t]\n",
    "\n",
    "    gscv = GridSearchCV(pipe, param_grid=parameters[t])\n",
    "\n",
    "    gscv.fit(x_train2, y_train2)\n",
    "    \n",
    "    print (t,\"score值:\",gscv.best_score_,\"最优参数列表:\", gscv.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确率: 0.843598090287\n"
     ]
    }
   ],
   "source": [
    "#使用最优参数看看正确率\n",
    "mms_best = MinMaxScaler()\n",
    "decision3 = DecisionTreeRegressor(criterion='mse', max_depth=4)\n",
    "\n",
    "x_train3, x_test3, y_train3, y_test3 = x_train1, x_test1, y_train1, y_test1\n",
    "x_train3 = mms_best.fit_transform(x_train3, y_train3)\n",
    "x_test3 = mms_best.transform(x_test3)\n",
    "decision3.fit(x_train3, y_train3)\n",
    "\n",
    "print (\"正确率:\", decision3.score(x_test3, y_test3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1深度，正确率0.32761\n",
      "2深度，正确率0.62189\n",
      "3深度，正确率0.78241\n",
      "4深度，正确率0.84360\n",
      "5深度，正确率0.83827\n",
      "6深度，正确率0.80707\n",
      "7深度，正确率0.80470\n",
      "8深度，正确率0.79651\n",
      "9深度，正确率0.81538\n",
      "10深度，正确率0.80637\n",
      "11深度，正确率0.79741\n",
      "12深度，正确率0.81638\n",
      "13深度，正确率0.78766\n",
      "14深度，正确率0.77954\n",
      "15深度，正确率0.82533\n",
      "16深度，正确率0.78892\n",
      "17深度，正确率0.78891\n",
      "18深度，正确率0.81576\n",
      "19深度，正确率0.80110\n"
     ]
    },
    {
     "data": {
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8PHR2GWJsQiovL6fAwEAKDg6miIgIGjx4sOq62l9//UXu7u7k6OhIfn5+agdR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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a59ba64550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看各个不同深度的错误率\n",
    "x_train4, x_test4, y_train4, y_test4 = x_train1, x_test1, y_train1, y_test1\n",
    "\n",
    "depths = np.arange(1, 20)\n",
    "err_list = []\n",
    "for d in depths:\n",
    "    clf = DecisionTreeRegressor(criterion='mse', max_depth=d)\n",
    "    clf.fit(x_train4, y_train4)\n",
    "    \n",
    "    score1 = clf.score(x_test4, y_test4)\n",
    "    err = 1 - score1\n",
    "    err_list.append(err)\n",
    "    print (\"%d深度，正确率%.5f\" % (d, score1))\n",
    "\n",
    "## 画图\n",
    "plt.figure(facecolor='w')\n",
    "plt.plot(depths, err_list, 'ro-', lw=3)\n",
    "plt.xlabel(u'决策树深度', fontsize=16)\n",
    "plt.ylabel(u'错误率', fontsize=16)\n",
    "plt.grid(True)\n",
    "plt.title(u'决策树层次太多导致的拟合问题(欠拟合和过拟合)', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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